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Liver Ailment Prediction Using Random Forest Model

computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Incentive Designs for Learning Agents to Stabilize Coupled Exogenous systems 63

Incentive Designs for Learning Agents to Stabilize Coupled E...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Certorio, Jair Martins, Nuno C. La, Richard J. Arcak, Murat Institute for Systems Research University of Maryland College Park Department of Electrical and Computer Engineering College ParkMD20740 United States University of California Department of Electrical Engineering and Computer Science BerkeleyCA94720 United States

ISBN: (纸本)9798350316339

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states. © 2024 IEEE.

关键词： Lyapunov functions

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Using Zodiacal Light For Spaceborne Calibration Of Polarimetric Imagers

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年 PP卷 1-12页

作者： Avitan, Or Schechner, Yoav Y. Behar, Ehud Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa Israel Department of Physics Technion-Israel Institute of Technology Haifa Israel

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realistic calibration sessions. We derive a forward model for polarimetric image formation. Based on it, we formulate an inverse problem for polarimetric calibration and self-calibration, as well as an algorithm for the solution. The methods here are demonstrated in simulations. Towards these simulations, we render polarized images of the sky, including ZL from space, polarimetric disturbances, and imaging noise. IEEE

关键词： Calibration

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A Digital Calibration Source for 21 cm Cosmology Telescopes

A Digital Calibration Source for 21 cm Cosmology Telescopes

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Bhopi, Kalyani Tyndall, Will Cole, Morgan Helfenbein, Mallory Bandura, Kevin Newburgh, Laura Lane Department of Computer Science and Electrical Engineering MorgantownWV United States Department of Physics Yale New HavenCT United States

ISBN: (纸本)9781946815200

Radio surveys of the red-shifted 21 cm emission line from neutral hydrogen provide a means to measure statistical cosmological signals that cannot be measured through other means. Many past experiments have shown that the major challenge for carrying out 21 cm measurements is the presence of bright foregrounds from the galaxy, and suppressing these foregrounds has become vital in 21 cm Cosmology. Attempts to separate signal and foreground components have led to new developments in hardware specifically dedicated to instrument calibration. The measurement of the instrument beam is implemented by flying radio calibration sources over an interferometric array to measure beam pattern. The calibration sources currently used in literature are broadband incoherent sources that can be detected as total power only, and no direct phase sensitivity is recorded. As a result, these instruments must contend with signal-to-noise issues where the beam has low response as well as the lack of a direct phase measurement without a reference antenna. © 2025 USNC-URSI.

关键词： Cosmology

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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Impact of Rain Heights on Rain-Induced Attenuation for Communication systems Operating at Ka and V Bands in Pretoria, South Africa 15

Impact of Rain Heights on Rain-Induced Attenuation for Commu...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Lawal, Yusuf Babatunde Tu, Chunling Van Wyk, Etienne A. Owolawi, Pius A. Ojo, Joseph Sunday Tshwane University of Technology Department of Computer Systems Engineering Soshanguve South South Africa Federal University of Technology Department of Physics Akure Nigeria

ISBN: (纸本)9798331507695

Wireless communication networks, such as 5G networks, inter-terrestrial, and earth-space links, transmit radio signals at high-frequency bands. However, the signal quality of radio communication systems operating at frequencies above 10 GHz in tropical and subtropical regions is often degraded due to rain-induced attenuation. Rain rate and rain height are the most influential meteorological parameters determining the level of attenuation. This research investigates the effects of seasonal variations in rain height on rain-induced attenuation and its impact on radio links operating at typical downlink and uplink frequencies in the Ka (30/20 GHz) and V (40/50 GHz) bands in Pretoria, South Africa. The research revealed that maximum rain heights experienced in the summer resulted in the worst rain-induced attenuation at all frequencies. The estimated attenuations would assist to determining the minimum fade margins required to achieve 99.9%, 99.99% and 99.999% signal availability annually at these frequencies in the study location. The results are expected to serve as a database for future planning of high-frequency link budgets. © 2025 IEEE.

关键词： Rain

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Explainability and Interpretability in Decision Trees and Agent Based Modelling When Approximating Financial Time Series. A Matter of Balance with Performance 8

Explainability and Interpretability in Decision Trees and Ag...

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8th International Conference on Computational Intelligence and Applications, ICCIA 2023

作者： Layfield, Colin Neri, Filippo University of Malta Department of Computer Information Systems Msida Malta University of Naples Department of Electrical and Computer Engineering Naples Italy

ISBN: (纸本)9798350339741

The paper discusses the notions of explainability and interpretability when using decision tree learning and agent based modeling to approximate financial time series. And how they related to the selected learning algorithm. As experimental context, the LFABS system for agent based modeling is used together with C4.5 for decision tree learning. The paper proposes the following definitions for interpretability: being able to make sense of a learning system output. And explainability: understanding how that output was generated. The study's goal is achieved by comparing the different knowledge representations used by the two systems. © 2023 IEEE.

关键词： Autonomous agents

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Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot 35

Assessing the Performance of AI-Generated Code: A Case Study...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Li, Shuang Cheng, Yuntao Chen, Jinfu Xuan, Jifeng He, Sen Shang, Weiyi Wuhan University School of Computer Science Wuhan China University of Arizona Department of Systems and Industrial Engineering Tucson United States University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350353884

The integration of Large Language Models (LLMs) into software development tools like GitHub Copilot holds the promise of transforming code generation processes. While AI-driven code generation presents numerous advantages for software development, code generated by large language models may introduce challenges related to security, privacy, and copyright issues. However, the performance implications of AI-generated code remain insufficiently explored. This study conducts an empirical analysis focusing on the performance regressions of code generated by GitHub Copilot across three distinct datasets: HumanEval, AixBench, and MBPP. We adopt a comprehensive methodology encompassing static and dynamic performance analyses to assess the effectiveness of the generated code. Our findings reveal that although the generated code is functionally correct, it frequently exhibits performance regressions compared to code solutions crafted by humans. We further investigate the code-level root causes responsible for these performance regressions. We identify four major root causes, i.e., inefficient function calls, inefficient looping, inefficient algorithm, and inefficient use of language features. We further identify a total of ten sub-categories of root causes attributed to the performance regressions of generated code. Additionally, we explore prompt engineering as a potential strategy for optimizing performance. The outcomes suggest that meticulous prompt designs can enhance the performance of AI-generated code. This research offers valuable insights contributing to a more comprehensive understanding of AI-assisted code generation. © 2024 IEEE.

关键词： Software design

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Sensitivity-based Vulnerability Assessment of State Estimation

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Journal of Modern Power systems and Clean Energy 2021年第4期9卷 886-896页

作者： Gonzalo E.Constante-Flores Antonio J.Conejo Jiankang Wang Department of Electrical and Computer Engineering The Ohio State UniversityColumbusUSA Department of Integrated Systems Engineering and the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusUSA

We propose a technique to assess the vulnerability of the power system state estimation. We aim at identifying the measurements that have a high potential of being the target of false data injection attacks. From the perspective of the adversary, such measurements have the following characteristics: ① being influential on the variable estimates;② corrupting their measured values is likely to be undetected. Additionally, such characteristics should not change significantly with the system operation condition. The proposed technique provides a systematic way of identifying the measurements with such characteristics. We illustrate our methodology on a 4-bus system, the New England 39-bus system, and the IEEE 118-bus test system, respectively.

关键词： False data injection attack state estimation sensitivity analysis singular value decomposition

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Cooperative nonlinear distributed model predictive control with dissimilar control horizons 63

Cooperative nonlinear distributed model predictive control w...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chanfreut, Paula Maestre, Jose M. Zhu, Quanyan Heemels, W.P.M.H. Maurice Eindhoven University of Technology Department of Mechanical Engineering Netherlands University of Seville Department of Systems and Automation Engineering Spain New York University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

In this paper, we introduce a nonlinear distributed model predictive control (DMPC) algorithm, which allows for dissimilar and time-varying control horizons among agents, thereby addressing a common limitation in current DMPC schemes. We consider cooperative agents with varying computational capabilities and operational objectives, each willing to manage varying numbers of optimization variables at each time step. Recursive feasibility and a non-increasing evolution of the optimal cost are proven for the proposed algorithm. Through numerical simulations on systems with three agents, we show that our approach effectively approximates the performance of traditional DMPC, while reducing the number of variables to be optimized. This advancement paves the way for a more decentralized yet coordinated control strategy in various applications, including power systems and traffic management. © 2024 IEEE.

关键词： Predictive control systems

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